Sequencing vs. amplification for the estimation of allele dosages in sugarcane (<i>Saccharum</i> spp.)

Jaimes, Hugo; Londoño, Alejandra; Saavedra‐Diaz, Carolina; Trujillo‐Montenegro, Jhon Henry; López‐Gerena, Jershon; Riascos, John J.; Aguilar, Fernando S.

doi:10.1002/aps3.11574

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…The dataset analyzed in this study comprises 220 genotypes (Jaimes et al, 2024) from the diverse panel of Centro de Investigación de la Caña de Azúcar, Cenicaña (Colombian Sugarcane Research Center). This panel includes 98 genotypes representing the phenotypic diversity of Cenicaña’s germplasm bank, 58 genotypes selected for their resistance or susceptibility to prevalent diseases and pests in Colombia, 33 genotypes encompassing introductions and commercial varieties, and 31 wild species from Saccharum officinarum, S. spontaneum, S. barberi, S. sinensi, and Erianthus spp.…”

Section: Methodsmentioning

confidence: 99%

“…The dataset analyzed in this study comprises 220 genotypes (Jaimes et al, 2024) Phenotypic data for sucrose accumulation (SA) and tons of cane per hectare (TCH), were collected during harvest (13 months after planting). Sucrose accumulation was measured by shredding 10 stalks per experimental unit to obtain the juice per sample (Laharrahondo and Torres, 1989).…”

Section: Plant Materials and Phenotypingmentioning

confidence: 99%

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Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Garcia-Abadillo,

Adunola,

Aguilar

et al. 2024

Preprint

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Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane’s yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6 × 186 = 1,116 phenotypes). Our study employed three predictive models, including environment and genotype as main effects, as well as the G×E interaction to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA (ρ= 0.611) and for TCH (ρ= 0.341) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72 = 12 × 6) being the most convenient cost-benefit combination.

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Section: Methodsmentioning

confidence: 99%

Section: Plant Materials and Phenotypingmentioning

confidence: 99%

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Garcia-Abadillo,

Adunola,

Aguilar

et al. 2024

Preprint

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Genetic association analysis in sugarcane (Saccharum spp.) for sucrose accumulation in humid environments in Colombia

Saavedra-Díaz,

Trujillo-Montenegro,

Jaimes

et al. 2024

BMC Plant Biol

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Background Sucrose accumulation in sugarcane is affected by several environmental and genetic factors, with plant moisture being of critical importance for its role in the synthesis and transport of sugars within the cane stalks, affecting the sucrose concentration. In general, rainfall and high soil humidity during the ripening stage promote plant growth, increasing the fresh weight and decreasing the sucrose yield in the humid region of Colombia. Therefore, this study aimed to identify markers associated with sucrose accumulation or production in the humid environment of Colombia through a genome-wide association study (GWAS). Results Sucrose concentration measurements were taken in 220 genotypes from the Cenicaña’s diverse panel at 10 (early maturity) and 13 (normal maturity) months after planting. For early maturity data was collected during plant cane and first ratoon, while at normal maturity it was during plant cane, first, and second ratoon. A total of 137,890 SNPs were selected after sequencing the 220 genotypes through GBS, RADSeq, and whole-genome sequencing. After GWAS analysis, a total of 77 markers were significantly associated with sucrose concentration at both ages, but only 39 were close to candidate genes previously reported for sucrose accumulation and/or production. Among the candidate genes, 18 were highlighted because they were involved in sucrose hydrolysis (SUS6, CIN3, CINV1, CINV2), sugar transport (i.e., MST1, MST2, PLT5, SUT4, ERD6 like), phosphorylation processes (TPS genes), glycolysis (PFP-ALPHA, HXK3, PHI1), and transcription factors (ERF12, ERF112). Similarly, 64 genes were associated with glycosyltransferases, glycosidases, and hormones. Conclusions These results provide new insights into the molecular mechanisms involved in sucrose accumulation in sugarcane and contribute with important genomic resources for future research in the humid environments of Colombia. Similarly, the markers identified will be validated for their potential application within Cenicaña’s breeding program to assist the development of breeding populations.

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Sparse testing designs for optimizing predictive ability in sugarcane populations

Garcia-Abadillo,

Adunola,

Aguilar

et al. 2024

Front. Plant Sci.

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Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane’s yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA (ρ=0.611) and for TCH (ρ=0.341) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

show abstract

Sequencing vs. amplification for the estimation of allele dosages in sugarcane (Saccharum spp.)

Cited by 3 publications

References 35 publications

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

Genetic association analysis in sugarcane (Saccharum spp.) for sucrose accumulation in humid environments in Colombia

Sparse testing designs for optimizing predictive ability in sugarcane populations

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